├── .gitattributes ├── .gitignore ├── 2023-24 ├── lda-embedding.ipynb ├── nlp │ ├── BERT │ │ ├── MLM.ipynb │ │ ├── NSP.ipynb │ │ ├── bert-explained.ipynb │ │ ├── img │ │ │ ├── .$bert_finetune.drawio.bkp │ │ │ ├── .$bert_pretrain.drawio.bkp │ │ │ ├── bert.drawio │ │ │ ├── bert.png │ │ │ ├── bert.svg │ │ │ ├── bert_finetune.drawio │ │ │ ├── bert_finetune_mnli.svg │ │ │ ├── bert_finetune_snli.svg │ │ │ ├── bert_finetune_squad.svg │ │ │ ├── bert_models.png │ │ │ ├── bert_pretrain.drawio │ │ │ ├── bert_pretrain_1.svg │ │ │ ├── bert_pretrain_2.svg │ │ │ ├── bert_variants.png │ │ │ ├── encoder bw.png │ │ │ ├── encoder.png │ │ │ └── gpt.png │ │ └── transformers_multi_label_classification.ipynb │ ├── README.md │ ├── born.ipynb │ ├── frequency.ipynb │ ├── gpt-generate.ipynb │ ├── imgs │ │ ├── word2vec.html │ │ └── wordnet.png │ ├── introduction-neural-networks.ipynb │ ├── language-modeling.ipynb │ ├── lda.ipynb │ ├── linear-classifiers.ipynb │ ├── markov-language-model.ipynb │ ├── nlp │ │ ├── __init__.py │ │ ├── data.py │ │ ├── datageneration.py │ │ └── languagemodels.py │ ├── pyproject.toml │ ├── pytorch │ │ ├── Deep_Learning_in_PyTorch.ipynb │ │ ├── Neural_Sentiment_Classification.ipynb │ │ ├── PyTorch_Basics.ipynb │ │ └── README.MD │ ├── rnn-classification.ipynb │ ├── seq2seq.ipynb │ ├── tests │ │ └── __init__.py │ ├── tokenization.ipynb │ ├── transformers │ │ ├── img │ │ │ ├── .$Transformer_Architecture_complete.drawio.bkp │ │ │ ├── .$attention.drawio.bkp │ │ │ ├── .$multihead-attn.drawio.bkp │ │ │ ├── .$multihead-attn.svg.bkp │ │ │ ├── .$query-key-value.drawio.bkp │ │ │ ├── .$word_embedding_0.svg.bkp │ │ │ ├── attention.drawio │ │ │ ├── attention.svg │ │ │ ├── multihead-attn.drawio │ │ │ ├── multihead-attn.svg │ │ │ ├── query-key-value.drawio │ │ │ ├── query-key-value.svg │ │ │ ├── word_embedding.drawio │ │ │ ├── word_embedding.png │ │ │ ├── word_embedding.svg │ │ │ └── word_embedding_0.svg │ │ └── transformers-explained.ipynb │ ├── vectorspace.ipynb │ ├── word2vec-from-scratch.ipynb │ ├── word2vec.ipynb │ └── wordnet.ipynb ├── projects │ └── nlp-projects-2023-24.pdf ├── slides │ ├── P1-10-sequence-learning.pdf │ ├── P1-3-evaluation.pdf │ ├── P1-4-latent-semantic-indexing.pdf │ ├── P1-5-topic_modeling.pdf │ ├── P1-6-unsupervised_classification.pdf │ ├── P1-7-supervised_classification.pdf │ ├── P1-8-language-models.pdf │ ├── P1-9-nlm-word2vec.pdf │ └── Text_Classification_with_Born_s_Rule___Slides.pdf ├── syllabus.md └── timetable.md ├── 2024-25 ├── README.md ├── data │ ├── README.md │ ├── biased-cv-doc.xlsx │ ├── biased-cv-tab.xlsx │ ├── get-movie-data.ipynb │ ├── get-recipes.ipynb │ ├── moviedata.py │ ├── political-bias.ipynb │ ├── roman_number-classification-test.csv │ ├── roman_number-classification.csv │ ├── roman_number-prediction-test.csv │ ├── roman_number-prediction.csv │ └── weather.json ├── imgs │ ├── clip.png │ ├── convolution.gif │ ├── cv-tasks.png │ ├── depth.png │ ├── diffusion.png │ ├── ffn-example.jpg │ ├── ffn.jpg │ ├── image-acquisition.png │ ├── image-generation.png │ ├── lenet.jpeg │ ├── mllm.png │ ├── perceptron-example.jpg │ ├── perceptron.jpg │ ├── pooling.png │ ├── raccoon.jpg │ ├── stablediffusion.png │ ├── stride.png │ ├── vit.gif │ ├── vit.png │ └── zero-padding.png ├── lectures │ └── NLP-Introduction.pdf ├── nlp │ ├── L0.0-introduction-to-text-data.ipynb │ ├── L0.1-wordpiece-example.ipynb │ ├── L0.2-search.ipynb │ ├── L1.0-classification.ipynb │ ├── L1.1-introduction-neural-networks.ipynb │ ├── L1.2-linear-classifiers-and-nn.ipynb │ ├── L1.3-pytorch-basics.ipynb │ ├── L1.4-deep-learning-pytorch.ipynb │ ├── L2.0-statistical-language-models.ipynb │ ├── L3.0-w2w-basics.ipynb │ ├── L3.1-pre-trained-w2v.ipynb │ ├── L4.0-sequence-learning-example.ipynb │ ├── L4.1-sequence-classification.ipynb │ ├── L4.2-sequence-generation.ipynb │ ├── L4.3-seq2seq-attention.ipynb │ ├── L5.1-gpt.ipynb │ ├── L6.0-image-processing.ipynb │ ├── L7.0-prompt-engineering.ipynb │ ├── L8.0-explainability.ipynb │ ├── L8.1-biases.ipynb │ ├── L8.2-bias-bert-classifier.ipynb │ ├── L8.3-shap_example.ipynb │ ├── L8.4-saliency-clip.ipynb │ ├── L8.5-concept-activation-vectors.ipynb │ ├── L8.6-bias-masking-task.ipynb │ ├── L8.7-bias-completion-task.ipynb │ ├── L9.0-llama-cpp-example.ipynb │ ├── L9.1-vllm-example.ipynb │ ├── L9.2-mlx-example.ipynb │ └── nlp │ │ ├── bert_classifier.py │ │ ├── gpt-requirements.txt │ │ ├── langmodel.py │ │ ├── neurnets.py │ │ ├── sequencedata.py │ │ ├── sequencelearning.py │ │ ├── utils.py │ │ ├── w2w_readers.py │ │ ├── wordbags.py │ │ ├── wordpiece.py │ │ └── wordvec.py └── projects │ └── nlp-projects-2024-25.md ├── 2025-26 ├── README.md ├── data │ ├── biased-cv-doc.xlsx │ ├── biased-cv-tab.xlsx │ ├── roman_number-classification-test.csv │ ├── roman_number-classification.csv │ ├── roman_number-prediction-test.csv │ ├── roman_number-prediction.csv │ └── weather.json ├── lectures │ ├── NLP-Introduction.pdf │ └── syllabus.md ├── nlp │ ├── L0.0-introduction-to-text-data.ipynb │ ├── L0.1-wordpiece-example.ipynb │ ├── L0.2-search.ipynb │ ├── L1.0-classification.ipynb │ ├── L1.1-introduction-neural-networks.ipynb │ ├── L1.2-linear-classifiers-and-nn.ipynb │ ├── L1.3-pytorch-basics.ipynb │ ├── L1.4-deep-learning-pytorch.ipynb │ ├── L10.0-biases.ipynb │ ├── L10.1-bias-bert-classifier.ipynb │ ├── L10.2-bias-masking-task.ipynb │ ├── L10.3-bias-completion-task.ipynb │ ├── L2.0-statistical-language-models.ipynb │ ├── L3.0-w2w-basics.ipynb │ ├── L3.1-pre-trained-w2v.ipynb │ ├── L4.0-sequence-learning-example.ipynb │ ├── L4.1-sequence-classification.ipynb │ ├── L4.2-sequence-generation.ipynb │ ├── L4.3-seq2seq-attention.ipynb │ ├── L5.0-tiny-transformer.ipynb │ ├── L5.1-transformers.ipynb │ ├── L5.2-tiny-bert.ipynb │ ├── L5.3-bert-explained.ipynb │ ├── L5.4-gpt.ipynb │ ├── L6.0-prompt-engineering.ipynb │ ├── L7.0-practical-llms.ipynb │ ├── L7.1-llama-cpp-example.ipynb │ ├── L7.2-vllm-example.ipynb │ ├── L7.3-mlx-example.ipynb │ ├── L8.0-image-processing.ipynb │ ├── L9.0-explainability.ipynb │ ├── L9.1-shap_example.ipynb │ ├── L9.2-saliency-clip.ipynb │ ├── L9.3-concept-activation-vectors.ipynb │ ├── img │ │ ├── .$Transformer_Architecture_complete.drawio.bkp │ │ ├── .$attention.drawio.bkp │ │ ├── .$multihead-attn.drawio.bkp │ │ ├── .$multihead-attn.svg.bkp │ │ ├── .$query-key-value.drawio.bkp │ │ ├── .$word_embedding_0.svg.bkp │ │ ├── attention.drawio │ │ ├── attention.svg │ │ ├── bert.drawio │ │ ├── bert.png │ │ ├── bert.svg │ │ ├── bert_finetune.drawio │ │ ├── bert_finetune_mnli.svg │ │ ├── bert_finetune_snli.svg │ │ ├── bert_finetune_squad.svg │ │ ├── bert_models.png │ │ ├── bert_pretrain.drawio │ │ ├── bert_pretrain_1.svg │ │ ├── bert_pretrain_2.svg │ │ ├── bert_variants.png │ │ ├── encoder bw.png │ │ ├── encoder.png │ │ ├── gpt.png │ │ ├── multihead-attn.drawio │ │ ├── multihead-attn.svg │ │ ├── quant-comparison.png │ │ ├── query-key-value.drawio │ │ ├── query-key-value.svg │ │ ├── word_embedding.drawio │ │ ├── word_embedding.png │ │ ├── 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